Abstract
Data assimilation algorithms require an accurate estimate of the uncertainty of the prior (background) field that cannot be adequately represented by the ensemble of numerical model simulations. Partially, this is due to the sampling error that arises from the use of a small number of ensemble members to represent the background-error covariance. It is also partially a consequence of the fact that the geophysical model does not represent its own error. Several mechanisms have been introduced so far to alleviate the detrimental effects of misrepresented ensemble covariances, allowing for the successful implementation of ensemble data assimilation techniques for atmospheric dynamics. One of the established approaches is additive inflation, which consists of perturbing each ensemble member with a sample from a given distribution. This results in a fixed rank of the effective model-error covariance matrix. In this article, a more flexible approach is introduced, where the model error samples are treated as additional synthetic ensemble members, which are used in the update step of data assimilation but are not forecast. This way, the rank of the model-error covariance matrix can be chosen independently of the ensemble. The effect of this altered additive inflation method on the performance of the filter is analyzed here in an idealized experiment. It is shown that the additional synthetic ensemble members can make it feasible to achieve convergence in an otherwise divergent parameter setting of data assimilation. The use of this method also allows for a less stringent localization radius.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Physik |
| Themengebiete: | 500 Naturwissenschaften und Mathematik > 530 Physik |
| ISSN: | 0035-9009 |
| Sprache: | Englisch |
| Dokumenten ID: | 66994 |
| Datum der Veröffentlichung auf Open Access LMU: | 19. Jul. 2019, 12:21 |
| Letzte Änderungen: | 04. Nov. 2020, 13:48 |
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